In this geometric visualisation, the value at the green circle multiplied by the horizontal distance between the red and blue circles is equal to the sum of the value at the red circle multiplied by the horizontal distance between the green and blue circles, and the value at the blue circle multiplied by the horizontal distance between the green and red circles.

If the two known points are given by the coordinates (x0,y0){\displaystyle (x_{0},y_{0})} and (x1,y1){\displaystyle (x_{1},y_{1})}, the linear interpolant is the straight line between these points. For a value x in the interval (x0,x1){\displaystyle (x_{0},x_{1})}, the value y along the straight line is given from the equation of slopes

which is the formula for linear interpolation in the interval (x0,x1){\displaystyle (x_{0},x_{1})}. Outside this interval, the formula is identical to linear extrapolation.

This formula can also be understood as a weighted average. The weights are inversely related to the distance from the end points to the unknown point; the closer point has more influence than the farther point. Thus, the weights are x−x0x1−x0{\textstyle {\frac {x-x_{0}}{x_{1}-x_{0}}}} and x1−xx1−x0{\textstyle {\frac {x_{1}-x}{x_{1}-x_{0}}}}, which are normalized distances between the unknown point and each of the end points. Because these sum to 1,

Linear interpolation on a data set (red points) consists of pieces of linear interpolants (blue lines).

Linear interpolation on a set of data points (x0, y0), (x1, y1), ..., (xn, yn) is defined as the concatenation of linear interpolants between each pair of data points. This results in a continuous curve, with a discontinuous derivative (in general), thus of differentiability classC0{\displaystyle C^{0}}.

That is, the approximation between two points on a given function gets worse with the second derivative of the function that is approximated. This is intuitively correct as well: the "curvier" the function is, the worse the approximations made with simple linear interpolation become.

Linear interpolation is often used to fill the gaps in a table. Suppose that one has a table listing the population of some country in 1970, 1980, 1990 and 2000, and that one wanted to estimate the population in 1994. Linear interpolation is an easy way to do this.

The basic operation of linear interpolation between two values is commonly used in computer graphics. In that field's jargon it is sometimes called a lerp. The term can be used as a verb or noun for the operation. e.g. "Bresenham's algorithm lerps incrementally between the two endpoints of the line."

Lerp operations are built into the hardware of all modern computer graphics processors. They are often used as building blocks for more complex operations: for example, a bilinear interpolation can be accomplished in three lerps. Because this operation is cheap, it's also a good way to implement accurate lookup tables with quick lookup for smooth functions without having too many table entries.

Comparison of linear and bilinear interpolation some 1- and 2-dimensional interpolations. Black and red/yellow/green/blue dots correspond to the interpolated point and neighbouring samples, respectively. Their heights above the ground correspond to their values.

If a C0 function is insufficient, for example if the process that has produced the data points is known be smoother than C0, it is common to replace linear interpolation with spline interpolation or, in some cases, polynomial interpolation.

Linear interpolation as described here is for data points in one spatial dimension. For two spatial dimensions, the extension of linear interpolation is called bilinear interpolation, and in three dimensions, trilinear interpolation. Notice, though, that these interpolants are no longer linear functions of the spatial coordinates, rather products of linear functions; this is illustrated by the clearly non-linear example of bilinear interpolation in the figure below. Other extensions of linear interpolation can be applied to other kinds of mesh such as triangular and tetrahedral meshes, including Bézier surfaces. These may be defined as indeed higher-dimensional piecewise linear function (see second figure below).

Example of bilinear interpolation on the unit square with the z values 0, 1, 1 and 0.5 as indicated. Interpolated values in between represented by colour.

A piecewise linear function in two dimensions (top) and the convex polytopes on which it is linear (bottom)

Many libraries and shading languages have a "lerp" helper-function, returning an interpolation between two inputs (v0, v1) for a parameter (t) in the closed unit interval [0, 1]:

// Imprecise method, which does not guarantee v = v1 when t = 1, due to floating-point arithmetic error.// This form may be used when the hardware has a native fused multiply-add instruction.floatlerp(floatv0,floatv1,floatt){returnv0+t*(v1-v0);}// Precise method, which guarantees v = v1 when t = 1.floatlerp(floatv0,floatv1,floatt){return(1-t)*v0+t*v1;}

This lerp function is commonly used for alpha blending (the parameter "t" is the "alpha value"), and the formula may be extended to blend multiple components of a vector (such as spatial x, y, z axes or r, g, b colour components) in parallel.